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C4000 | The average value becomes more and more precise as the number of measurements increases. Although the uncertainty of any single measurement is always ∆, the uncertainty in the mean ∆ avg becomes smaller (by a factor of 1/√) as more measurements are made. | |
C4001 | Statistics provide a valuable source of evidence to support the initiation of new policy or the alteration of an existing policy or program. Once an issue has been identified, it is then necessary to analyse the extent of the issue, and determine what urgency there is for the issue to be addressed. | |
C4002 | Imbalanced data sets are a special case for classification problem where the class distribution is not uniform among the classes. Typically, they are composed by two classes: The majority (negative) class and the minority (positive) class. | |
C4003 | How to Prevent OverfittingCross-validation. Cross-validation is a powerful preventative measure against overfitting. Train with more data. It won't work every time, but training with more data can help algorithms detect the signal better. Remove features. Early stopping. Regularization. Ensembling. | |
C4004 | Multiple regression (an extension of simple linear regression) is used to predict the value of a dependent variable (also known as an outcome variable) based on the value of two or more independent variables (also known as predictor variables). | |
C4005 | Linear filters process time-varying input signals to produce output signals, subject to the constraint of linearity. Since linear time-invariant filters can be completely characterized by their response to sinusoids of different frequencies (their frequency response), they are sometimes known as frequency filters. | |
C4006 | In information theory, the entropy of a random variable is the average level of "information", "surprise", or "uncertainty" inherent in the variable's possible outcomes. The minimum surprise is when p = 0 or p = 1, when the event is known and the entropy is zero bits. | |
C4007 | Artificial intelligence (AI) is a branch of computer science. Most AI programs are not used to control robots. Even when AI is used to control robots, the AI algorithms are only part of the larger robotic system, which also includes sensors, actuators, and non-AI programming. | |
C4008 | A significant result indicates that your data are significantly heteroscedastic, and thus the assumption of homoscedasticity in the regression residuals is violated. In your case the data violate the assumption of homoscedasticity, as your p value is 8.6⋅10−28. The e is standard scientific notation for powers of 10. | |
C4009 | The one-sample Z test is used only for tests of the sample mean. Thus, our hypothesis tests whether the average of our sample (M) suggests that our students come from a population with a know mean (m) or whether it comes from a different population. | |
C4010 | An inference network is a flexible construction for parameterizing approximating distributions during inference. | |
C4011 | The classic approach to the multiple comparison problem is to control the familywise error rate. Instead of setting the critical P level for significance, or alpha, to 0.05, you use a lower critical value. | |
C4012 | Machine learning has a limited scope. AI is working to create an intelligent system which can perform various complex tasks. Machine learning is working to create machines that can perform only those specific tasks for which they are trained. AI system is concerned about maximizing the chances of success. | |
C4013 | SVM algorithms use a set of mathematical functions that are defined as the kernel. The function of kernel is to take data as input and transform it into the required form. Different SVM algorithms use different types of kernel functions. These functions can be different types. | |
C4014 | In machine learning and natural language processing, a topic model is a type of statistical model for discovering the abstract "topics" that occur in a collection of documents. Topic modeling is a frequently used text-mining tool for discovery of hidden semantic structures in a text body. | |
C4015 | Data Science and Data analytics, the next big thing in Information Science. It is estimated that by 2020 the proportion of jobs in data analytics, data science, and machine learning will be 3x more than all other technical jobs. This is going to be a real gold mine for future professionals in the data analytics field. | |
C4016 | Sensitivity is a measure of the proportion of actual positive cases that got predicted as positive (or true positive). This implies that there will be another proportion of actual positive cases, which would get predicted incorrectly as negative (and, thus, could also be termed as the false negative). | |
C4017 | 1 Answer. A probability distribution is the theoretical outcome of an experiment whereas a sampling distribution is the real outcome of an experiment. | |
C4018 | Decision tree builds regression or classification models in the form of a tree structure. The topmost decision node in a tree which corresponds to the best predictor called root node. Decision trees can handle both categorical and numerical data. | |
C4019 | In mathematics, a Fourier transform (FT) is a mathematical transform that decomposes a function (often a function of time, or a signal) into its constituent frequencies, such as the expression of a musical chord in terms of the volumes and frequencies of its constituent notes. | |
C4020 | Nonresponse bias is the bias that results when respondents differ in meaningful ways from nonrespondents. Response rate is often low, making mail surveys vulnerable to nonresponse bias. Voluntary response bias. Voluntary response bias occurs when sample members are self-selected volunteers, as in voluntary samples. | |
C4021 | Time series is ordered data. So the validation data must be ordered to. Forward chaining ensures this. | |
C4022 | The Q statistic is used to try to partition the variability we see between studies into variability that is due to random variation, and variability that is due to potential differences between the studies. This is really similar to the way we partition variance when doing an ANOVA. | |
C4023 | How to Deal with MulticollinearityRemove some of the highly correlated independent variables.Linearly combine the independent variables, such as adding them together.Perform an analysis designed for highly correlated variables, such as principal components analysis or partial least squares regression. | |
C4024 | AlphaGo and its successors use a Monte Carlo tree search algorithm to find its moves based on knowledge previously acquired by machine learning, specifically by an artificial neural network (a deep learning method) by extensive training, both from human and computer play. | |
C4025 | Correlation analysis explores the association between two or more variables and makes inferences about the strength of the relationship. Technically, association refers to any relationship between two variables, whereas correlation is often used to refer only to a linear relationship between two variables. | |
C4026 | First, after looking around on the web, it seems that there is no way to compute a (discrete) Fourier transform through a neural network. You can hack it by hard-coding the thing to include the Fourier constants for the transform and then get a decent result. | |
C4027 | A saddle point of a matrix is an element which is both the largest element in its column and the smallest element in its row. | |
C4028 | So, for data, structured data is easily organizable and follows a rigid format; unstructured is complex and often qualitative information that is impossible to reduce to or organize in a relational database and semi-structured data has elements of both. | |
C4029 | Softmax is a non-linear activation function, and is arguably the simplest of the set. In this expression, zi is the current value. The denominator in the expression is the sum across every value passed to a node in the layer. | |
C4030 | While a P value can inform the reader whether an effect exists, the P value will not reveal the size of the effect. In reporting and interpreting studies, both the substantive significance (effect size) and statistical significance (P value) are essential results to be reported. | |
C4031 | The DQN Agent The DQN (Deep Q-Network) algorithm was developed by DeepMind in 2015. It was able to solve a wide range of Atari games (some to superhuman level) by combining reinforcement learning and deep neural networks at scale. | |
C4032 | CNNs | |
C4033 | Explanation: The two types of Fourier series are- Trigonometric and exponential. | |
C4034 | Yes, using the additional survey information from part (b) dramatically reduces the sample size. The required sample size decreases dramatically from 423 to 50, with the addition of the survey information. | |
C4035 | The mean of the sampling distribution of the mean is the mean of the population from which the scores were sampled. Therefore, if a population has a mean μ, then the mean of the sampling distribution of the mean is also μ. The symbol μM is used to refer to the mean of the sampling distribution of the mean. | |
C4036 | Consistency of an estimator means that as the sample size gets large the estimate gets closer and closer to the true value of the parameter. Unbiasedness is a finite sample property that is not affected by increasing sample size. An estimate is unbiased if its expected value equals the true parameter value. | |
C4037 | Analysis of variance (ANOVA) can determine whether the means of three or more groups are different. ANOVA uses F-tests to statistically test the equality of means. As in my posts about understanding t-tests, I'll focus on concepts and graphs rather than equations to explain ANOVA F-tests. | |
C4038 | Linear Regression is a machine learning algorithm based on supervised learning. It performs a regression task. Regression models a target prediction value based on independent variables. Linear regression performs the task to predict a dependent variable value (y) based on a given independent variable (x). | |
C4039 | Backpropagation is a short form for "backward propagation of errors." It is a standard method of training artificial neural networks. This method helps to calculate the gradient of a loss function with respects to all the weights in the network. | |
C4040 | A learning curve is a concept that graphically depicts the relationship between the cost and output over a defined period of time, normally to represent the repetitive task of an employee or worker. | |
C4041 | Negative sampling is a technique used to train machine learning models that generally have several order of magnitudes more negative observations compared to positive ones. And in most cases, these negative observations are not given to us explicitly and instead, must be generated somehow. | |
C4042 | As the names suggest, pre-pruning or early stopping involves stopping the tree before it has completed classifying the training set and post-pruning refers to pruning the tree after it has finished. | |
C4043 | A conditional probability estimate is a probability estimate that we make given or assuming the occurrence of some other event. In this case we might start with an estimate that the probability of rain is 30% and then make a conditional probability estimate that the probability of rain given a cloudy sky is 65%. | |
C4044 | Face validity refers to the extent to which a test appears to measure what it is intended to measure. A test in which most people would agree that the test items appear to measure what the test is intended to measure would have strong face validity. | |
C4045 | Although random sampling is generally the preferred survey method, few people doing surveys use it because of prohibitive costs; i.e., the method requires numbering each member of the survey population, whereas nonrandom sampling involves taking every nth member. | |
C4046 | The mean squared error tells you how close a regression line is to a set of points. It does this by taking the distances from the points to the regression line (these distances are the “errors”) and squaring them. The squaring is necessary to remove any negative signs. | |
C4047 | Simply put, an activation function is a function that is added into an artificial neural network in order to help the network learn complex patterns in the data. When comparing with a neuron-based model that is in our brains, the activation function is at the end deciding what is to be fired to the next neuron. | |
C4048 | more The range of each group of data. Example: you measure the length of leaves on a rose bush. Some are less than 1 cm, and the longest is 9 cm. | |
C4049 | In statistical terms, the posterior probability is the probability of event A occurring given that event B has occurred. Bayes' theorem thus gives the probability of an event based on new information that is, or may be related, to that event. | |
C4050 | The Backpropagation algorithm looks for the minimum value of the error function in weight space using a technique called the delta rule or gradient descent. The weights that minimize the error function is then considered to be a solution to the learning problem. | |
C4051 | Optimal control deals with the problem of finding a control law for a given system such that a certain optimality criterion is achieved. A control problem includes a cost functional that is a function of state and control variables. | |
C4052 | Data Preprocessing is a technique that is used to convert the raw data into a clean data set. In other words, whenever the data is gathered from different sources it is collected in raw format which is not feasible for the analysis. | |
C4053 | The random forest is a model made up of many decision trees. Rather than just simply averaging the prediction of trees (which we could call a “forest”), this model uses two key concepts that gives it the name random: Random sampling of training data points when building trees. | |
C4054 | Example question: Find a critical value for a 90% confidence level (Two-Tailed Test). Step 1: Subtract the confidence level from 100% to find the α level: 100% – 90% = 10%. Step 2: Convert Step 1 to a decimal: 10% = 0.10. Step 3: Divide Step 2 by 2 (this is called “α/2”). | |
C4055 | The covariance matrix provides a useful tool for separating the structured relationships in a matrix of random variables. This can be used to decorrelate variables or applied as a transform to other variables. It is a key element used in the Principal Component Analysis data reduction method, or PCA for short. | |
C4056 | Descriptive statistics are broken down into measures of central tendency and measures of variability (spread). Measures of central tendency include the mean, median and mode, while measures of variability include standard deviation, variance, minimum and maximum variables, and kurtosis and skewness. | |
C4057 | A Bernouilli distribution is a discrete probability distribution for a Bernouilli trial — a random experiment that has only two outcomes (usually called a “Success” or a “Failure”). The expected value for a random variable, X, from a Bernoulli distribution is: E[X] = p. For example, if p = . 04, then E[X] = 0.4. | |
C4058 | A radial basis function (RBF) is a real-valued function whose value depends only on the distance between the input and some fixed point, either the origin, so that , or some other fixed point , called a center, so that . Any function that satisfies the property is a radial function. | |
C4059 | There is really only one advantage to using a random forest over a decision tree: It reduces overfitting and is therefore more accurate. | |
C4060 | Data science is an umbrella term for a group of fields that are used to mine large datasets. Data analytics software is a more focused version of this and can even be considered part of the larger process. Analytics is devoted to realizing actionable insights that can be applied immediately based on existing queries. | |
C4061 | The following seven techniques can help you, to train a classifier to detect the abnormal class.Use the right evaluation metrics. Resample the training set. Use K-fold Cross-Validation in the right way. Ensemble different resampled datasets. Resample with different ratios. Cluster the abundant class. Design your own models. | |
C4062 | 15:3426:12Suggested clip · 115 secondsConvolutional Neural Network in Matlab - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C4063 | The standard error of the regression (S), also known as the standard error of the estimate, represents the average distance that the observed values fall from the regression line. Conveniently, it tells you how wrong the regression model is on average using the units of the response variable. | |
C4064 | The outcome variable is also called the response or dependent variable, and the risk factors and confounders are called the predictors, or explanatory or independent variables. In regression analysis, the dependent variable is denoted "Y" and the independent variables are denoted by "X". | |
C4065 | A null hypothesis is a type of conjecture used in statistics that proposes that there is no difference between certain characteristics of a population or data-generating process. The alternative hypothesis proposes that there is a difference. | |
C4066 | The linear Discriminant analysis estimates the probability that a new set of inputs belongs to every class. The output class is the one that has the highest probability. That is how the LDA makes its prediction. LDA uses Bayes' Theorem to estimate the probabilities. | |
C4067 | A Generative Model learns the joint probability distribution p(x,y). It predicts the conditional probability with the help of Bayes Theorem. A Discriminative model learns the conditional probability distribution p(y|x). Both of these models were generally used in supervised learning problems. | |
C4068 | Cross-entropy loss, or log loss, measures the performance of a classification model whose output is a probability value between 0 and 1. Cross-entropy loss increases as the predicted probability diverges from the actual label. | |
C4069 | Decision theory is the science of making optimal decisions in the face of uncertainty. Statistical decision theory is concerned with the making of decisions when in the presence of statistical knowledge (data) which sheds light on some of the uncertainties involved in the decision problem. | |
C4070 | The purpose of cluster analysis is to place objects into groups, or clusters, suggested by the data, not defined a priori, such that objects in a given cluster tend to be similar to each other in some sense, and objects in different clusters tend to be dissimilar. | |
C4071 | There is no direct evidence that the brain uses a backprop-like algorithm for learning. Past work has shown, however, that backprop-trained models can account for observed neural responses, such as the response properties of neurons in the posterior parietal cortex68 and primary motor cortex69. | |
C4072 | From the mathematical point of view, linear regression and ANOVA are identical: both break down the total variance of the data into different “portions” and verify the equality of these “sub-variances” by means of a test (“F” Test). | |
C4073 | When there are two or more independent variables, it is called multiple regression. | |
C4074 | conditions—Random, Normal, and Independent—is. important when constructing a confidence interval. | |
C4075 | Do you know how to choose the right machine learning algorithm among 7 different types?1-Categorize the problem. 2-Understand Your Data. Analyze the Data. Process the data. Transform the data. 3-Find the available algorithms. 4-Implement machine learning algorithms. 5-Optimize hyperparameters.More items | |
C4076 | Systematic sampling involves selecting fixed intervals from the larger population to create the sample. Cluster sampling divides the population into groups, then takes a random sample from each cluster. | |
C4077 | Moments are a set of statistical parameters to measure a distribution. Four moments are commonly used: 1st, Mean: the average. 2d, Variance: Standard deviation is the square root of the variance: an indication of how closely the values are spread about the mean. | |
C4078 | Naive Bayes classifier (Russell, & Norvig, 1995) is another feature-based supervised learning algorithm. It was originally intended to be used for classification tasks, but with some modifications it can be used for regression as well (Frank, Trigg, Holmes, & Witten, 2000) . | |
C4079 | intra-observer (or within observer) reliability; the degree to which measurements taken by the same observer are consistent, • inter-observer (or between observers) reliability; the degree to which measurements taken by different observers are similar. | |
C4080 | The binomial theorem is valid more generally for any elements x and y of a semiring satisfying xy = yx. The theorem is true even more generally: alternativity suffices in place of associativity. The binomial theorem can be stated by saying that the polynomial sequence {1, x, x2, x3, } is of binomial type. | |
C4081 | The Deep Dream Generator is a computer vision platform that allows users to input photos into the program and transform them through an artificial intelligence algorithm. In simple terms, many levels of neural networks process the images input into the program. | |
C4082 | 0:5218:40Suggested clip · 82 secondsChain Rule For Finding Derivatives - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C4083 | Decision tree builds classification or regression models in the form of a tree structure. The topmost decision node in a tree which corresponds to the best predictor called root node. Decision trees can handle both categorical and numerical data. | |
C4084 | The algorithm works iteratively to assign each data point to one of K groups based on the features that are provided. The results of the K-means clustering algorithm are: The centroids of the K clusters, which can be used to label new data. Labels for the training data (each data point is assigned to a single cluster) | |
C4085 | Bayesian network models capture both conditionally dependent and conditionally independent relationships between random variables. Models can be prepared by experts or learned from data, then used for inference to estimate the probabilities for causal or subsequent events. | |
C4086 | mAP (mean Average Precision) for Object DetectionPrecision & recall.Precision measures how accurate is your predictions. Recall measures how good you find all the positives. IoU (Intersection over union)Precision is the proportion of TP = 2/3 = 0.67.Recall is the proportion of TP out of the possible positives = 2/5 = 0.4. | |
C4087 | Descriptive statistics uses the data to provide descriptions of the population, either through numerical calculations or graphs or tables. Inferential statistics makes inferences and predictions about a population based on a sample of data taken from the population in question. | |
C4088 | In a skewed distribution, the upper half and the lower half of the data have a different amount of spread, so no single number such as the standard deviation could describe the spread very well. | |
C4089 | In ordinary least squares, the relevant assumption of the classical linear regression model is that the error term is uncorrelated with the regressors. The presence of omitted-variable bias violates this particular assumption. The violation causes the OLS estimator to be biased and inconsistent. | |
C4090 | The standard normal distribution table provides the probability that a normally distributed random variable Z, with mean equal to 0 and variance equal to 1, is less than or equal to z. It does this for positive values of z only (i.e., z-values on the right-hand side of the mean). | |
C4091 | The dependent variable must be continuous (interval/ratio). The observations are independent of one another. The dependent variable should be approximately normally distributed. The dependent variable should not contain any outliers. | |
C4092 | Choosing a statistical testType of DataCompare one group to a hypothetical valueOne-sample ttestWilcoxon testCompare two unpaired groupsUnpaired t testMann-Whitney testCompare two paired groupsPaired t testWilcoxon testCompare three or more unmatched groupsOne-way ANOVAKruskal-Wallis test6 more rows• | |
C4093 | 8 Powerful Tricks That Make You Grasp New Concepts Faster1) Use mental associations. Colours, acronyms and word associations can be especially useful tools to help you hold on to thoughts, patterns and concepts. 2) Apply the 80/20 principle. 3) Break it down. 4) Write it down. 5) Connect existing knowledge. 6) Try Brain exercises. 7) Learn your way. 8) Teach other people. | |
C4094 | After a performing a test, scientists can: Reject the null hypothesis (meaning there is a definite, consequential relationship between the two phenomena), or. Fail to reject the null hypothesis (meaning the test has not identified a consequential relationship between the two phenomena) | |
C4095 | Dimensionality reduction is the process of reducing the number of random variables or attributes under consideration. High-dimensionality data reduction, as part of a data pre-processing-step, is extremely important in many real-world applications. | |
C4096 | Gradient descent is an optimization algorithm that's used when training a machine learning model. It's based on a convex function and tweaks its parameters iteratively to minimize a given function to its local minimum. | |
C4097 | The K-means clustering algorithm is used to find groups which have not been explicitly labeled in the data. This can be used to confirm business assumptions about what types of groups exist or to identify unknown groups in complex data sets. | |
C4098 | Maximum entropy is the state of a physical system at greatest disorder or a statistical model of least encoded information, these being important theoretical analogs. | |
C4099 | Epsilon is used when we are selecting specific actions base on the Q values we already have. As an example if we select pure greedy method ( epsilon = 0 ) then we are always selecting the highest q value among the all the q values for a specific state. |
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